Methods known until now obtain speech recognition in a noisy environment, e.g. by performing a noise reduction.
A method for noise reduction is e.g. a cepstral noise reduction by means of a multi-layer network based on LPC (Linear Predictive Coding) cepstral coefficients. Here, a noise reduction network performs nonlinear auto-associative mapping of a number of noisy cepstral coefficients into a number of noise-free cepstral coefficients in the cepstral range.
The average speech recognition rate is about 65% when the described noise reduction is performed ("A Cepstral Noise Reduction Multi-layer Neural Network; Helge B. D. Sorensen; ICASSP91; Toronto, Canada; May 14-17, 1991).
Another method of noise reduction in the cepstral range is based on a self-structuring universal network. Such a network makes noise reduction possible in the following three models. The first model performs a spectral analysis of the spoken language. The second model is a self-structuring neural noise reduction model SNNR (Self-structuring Neural Noise Reduction). The already noise-reduced output of the SNNR network is expanded by a so-called Hidden Control Neural Network (HCNN) ("A Self-structuring Neural Noise Reduction Model"; Helge B. D. Sorensen and Uwe Hartmann; University of Aalberg; Denmark, after May 1991).
An also known method of noise reduction utilizes so-called connectionist models. By means of an algorithm, a network consisting of four layers is trained to map noisy signals into noise-free signals. In this way, the network is in a position to learn noise reduction. It is also in a position to separate noisy signals, which are not part of the trained signals, from the noise. Such a network is also in a position to separate noisy signals, which are also not trained, from noise ("Noise Reduction Using Connectionist Models"; Shin'ichi Tamura and Alex Waibel; Osaka, Japan; ICASSP88; April 1988).
Furthermore, different network topologies and different variations of training algorithms are tested and optimized when using a neural network for noise reduction. This improves the robustness of speech recognition against environmental noise ("Building Blocks for a Neural Noise Reduction Network for Robust Speech Recognition"; Michael Trompf; Proceedings of EUSIPCA 1992; Brussels, Belgium; Aug. 24-27, 1992).